The phenotype, psychotype and genotype of bruxism

Abstract. Bruxism is a jaw muscle activity that involves physio-pathological, psycho-social, hereditary and genetic factors. The purpose of this study was to determine the associations between self-reported bruxism, anxiety, and neuroticism personality trait with the rs6313 polymorphism in the gene HTR2A. A sample of 171 subjects of both sexes (14-53 years of age) was included. The control group (group 1, n=60) exhibited no signs or symptoms of bruxism. The case group had signs and symptoms of bruxism (n=112) and was subdivided into group 2, bruxism during sleep (n=22); group 3, awake bruxism (n=44); and group 4 combined bruxism (n=46). As diagnostic tools, the Self-Reported Bruxism Questionnaire (SBQ), the Beck Anxiety Inventory (BAI) and the Eysenck Personality Questionnaire Revised-Abbreviated (EPQR-A) were used. HTR2A (rs6313) SNPs were determined by qPCR for all the participants. The packages SPSS, maxLik and EPI-INFO were used for data analysis. The combined bruxism group reported higher scores in bruxism symptoms, mean = 32.21; anxiety symptoms, mean = 14.80; and neuroticism, mean = 3.26. Combined bruxism was associated with a higher degree of neuroticism (OR=15.0; CI 1.52-148.32) and anxiety in grade 3-moderate (OR=3.56; CI 1.27-10.03), and grade 4-severe (OR=8.40; CI 1.45-48.61), as determined using EPISODE computer software. Genotypic homogeneity analysis revealed no significant differences in allele frequency (P=0.612) among the four groups. The population was in Hardy-Weinberg equilibrium (maxLik package). In conclusion, the three instruments confirm traits of bruxism, anxiety and neuroticism in individuals with bruxism. These data were ratified when the sample was divided by genotypic homogeneity. On the other hand, there was no significant difference between the groups in the SNPs rs6313 from the HTR2A gene

Evaluation of in vivo pathogenicity of Candida parapsilosis, Candida orthopsilosis, and Candida metapsilosis with different enzymatic profiles in a murine model of disseminated candidiasis

Six isolates of the Candida parapsilosis complex with different enzymatic profiles were used to induce systemic infection in immunocompetent BALB/c mice. Fungal tissue burden was determined on days 2, 5, 10, and 15 post challenge. The highest fungal load irrespective of post-infection day was detected in the kidney, followed by the spleen, lung,andliver,withatendencyforthefungalburdentodecreasebyday15inallgroups. Significant differences among the strains were not detected, suggesting that the three species of the “psilosis” group possess a similar pathogenic potential in disseminated candidiasis regardless of their enzymatic profile

Evaluation of in vivo pathogenicity of Candida parapsilosis, Candida orthopsilosis, and Candida metapsilosis with different enzymatic profiles in a murine model of disseminated candidiasis

Six isolates of the Candida parapsilosis complex with different enzymatic profiles were used to induce systemic infection in immunocompetent BALB/c mice. Fungal tissue burden was determined on days 2, 5, 10, and 15 post challenge. The highest fungal load irrespective of post-infection day was detected in the kidney, followed by the spleen, lung,andliver,withatendencyforthefungalburdentodecreasebyday15inallgroups. Significant differences among the strains were not detected, suggesting that the three species of the “psilosis” group possess a similar pathogenic potential in disseminated candidiasis regardless of their enzymatic profile

Multivariate feature selection of image descriptors data for breast cancer with computer-assisted diagnosis

Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions

Multivariate feature selection of image descriptors data for breast cancer with computer-assisted diagnosis

Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions

E-Cadherin gene expression in oral cancer : clinical and prospective data

Low protein expression of E-cadherin in oral squamous cell carcinoma (OSCC) has been associated with clinical and histopathological traits such as metastases, recurrence, low survival and poor tumor differentiation, and it is considered a high-risk marker of malignancy. However, it is still unknown whether low expression of E-cadherin is also present at the mRNA level in OSCC cases. Objective: The aim of this study was to compare E-cadherin mRNA expression in OSCC patients and controls and to correlate the expression with clinical and prospective characteristics. Forty patients and 40 controls were enrolled. E-cadherin mRNA expression was evaluated by quantitative real-time polymerase chain reaction using TaqMan probes. E-cadherin mRNA expression was significantly decreased in OSCC patients compared to that of controls (p<0.001). Whereas no significant association between clinical parameters and E-cadherin expression levels was observed, we noted lower E-cadherin expression levels in patients with positive lymph node metastasis. E-cadherin mRNA expression was markedly diminished in OSCC, in agreement with previous results that examined E-cadherin expression at the protein level. E-cadherin is downregulated in the early clinical stages of OSCC, and its mRNA levels do not change significantly in the advanced stages, suggesting that there is limited usefulness of this parameter for predicting disease progression

A comparison of back propagation and Generalized Regression Neural Networks performance in neutron spectrometry

The process of unfolding the neutron energy spectrum has been subject of research for many years. Monte Carlo, iterative methods, the bayesian theory, the principle of maximum entropy are some of the methods used. The drawbacks associated with traditional unfolding procedures have motivated the research of complementary approaches. Back Propagation Neural Networks (BPNN), have been applied with success in neutron spectrometry and dosimetry domains, however, the structure and learning parameters are factors that highly impact in the networks performance. In ANN domain, Generalized Regression Neural Network (GRNN) is one of the simplest neural networks in term of network architecture and learning algorithm. The learning is instantaneous, requiring no time for training. Opposite to BPNN, a GRNN would be formed instantly with just a 1-pass training on the development data. In the network development phase, the only hurdle is to optimize the hyper-parameter, which is known as sigma, governing the smoothness of the network. The aim of this work was to compare the performance of BPNN and GRNN in the solution of the neutron spectrometry problem. From results obtained it can be observed that despite the very similar results, GRNN performs better than BPNN

An Analysis of Audio Features to Develop a Human Activity Recognition Model Using Genetic Algorithms, Random Forests, and Neural Networks

This work presents a human activity recognition (HAR) model based on audio features. The use of sound as an information source for HAR models represents a challenge because sound wave analyses generate very large amounts of data. However, feature selection techniques may reduce the amount of data required to represent an audio signal sample. Some of the audio features that were analyzed include Mel-frequency cepstral coefficients (MFCC). Although MFCC are commonly used in voice and instrument recognition, their utility within HAR models is yet to be confirmed, and this work validates their usefulness. Additionally, statistical features were extracted from the audio samples to generate the proposed HAR model. The size of the information is necessary to conform a HAR model impact directly on the accuracy of the model. This problem also was tackled in the present work; our results indicate that we are capable of recognizing a human activity with an accuracy of 85% using the HAR model proposed. This means that minimum computational costs are needed, thus allowing portable devices to identify human activities using audio as an information source

Patients with advanced oral squamous cell carcinoma have high levels of soluble E-cadherin in the saliva

The objective of this study was to assess the potential clinical value of the concentration of soluble salivary E-cadherin (sE-cadherin) compared with the clinical value of the presence of membranous E-cadherin (mE-cadherin) in oral squamous cell carcinoma tumor tissues. Data regarding patient demographics, clinical stage, saliva and tumor tissue samples were collected. The saliva was analyzed for sE-cadherin protein levels and was compared to the mE-cadherin immunohistochemical expression levels in tumor tissues, which were assessed via the HercepTest® method. Patients without cancer were included in the study as a control group for comparisons of the sE-cadherin levels. sE-cadherin levels in the saliva of patients without cancer were lower than those in patients with cancer, and the difference was statistically significant (p=0.031). Low mE-cadherin expression was statistically significantly associated with lymph node positivity (p=0.015) and advanced clinical stage (p=0.001). The inverse relationship between mE-cadherin and sE-cadherin was significant in terms of lymph node positivity (p=0.014) and advanced clinical stage (p=0.037). The results suggest that sE-cadherin levels are significantly increased in patients with oral cancer and that its low expression within the membrane as well as the progression of the disease appear to be inversely associated with levels of sE-cadherin in the saliva